Medical Image Processing by using Soft Computing Methods and Information Fusion

Medical images are increasingly being used within healthcare for diagnosis, planning treatment, guiding treatment and monitoring disease progression. Technically, medical imaging mainly processes uncertain, missing, ambiguous, complementary, inconsistent, redundant contradictory, distorted data and information has a strong structural character. As a general approach, the understanding of any image involves the matching of features extracted from the image with pre-stored models. The production of a high-level symbolic model requires the representation of knowledge about the objects to be modeled, their relationships, and how and when to use the information stored within the model. This paper reports new (semi)automated methods for the segmentation and classification of medical images using soft computing techniques (e.g. fuzzy logic, neural networks, genetic algorithms), information fusion and specific domain knowledge. Fuzzy logic acts as a unified framework for representing and processing both numerical and symbolic information (“hybridization”), as well as structural information constituted mainly by spatial relationships in biomedical imaging. Promising results show the superiority of the soft computing and knowledge-based approach over best traditional techniques in terms of segmentation errors. The classification of different anatomic structures is made by implementing rules yielded both by domain literature and by medical experts. Though the proposed methodology has been implemented and successfully used for model-driven in the domain of medical imaging, the deployed methods are generic and applicable to any structure that can be defined by expert knowledge and morphological image analysis. Key-Words: medical image processing, artificial intelligence, soft-computing, knowledge-based processing, data fusion.